Recent results concerning asymptotic Bayes-optimality under sparsity (ABOS) of multiple testing procedures are extended to fairly generally distributed effect sizes under the alternative. An asymptotic framework is considered where both the number of tests m and the sample size m go to infinity, while the fraction p of true alternatives converges to zero. It is shown that under mild restrictions on the loss function nontrivial asymptotic inference is possible only if n increases to infinity at least at the rate of log m. Based on this assumption precise conditions are given under which the Bonferroni correction with nominal Family Wise Error Rate (FWER) level alpha and the Benjamini- Hochberg procedure (BH) at FDR level alpha are asymptotically optimal. When n is proportional to log m then alpha can remain fixed, whereas when n increases to infinity at a quicker rate, then alpha has to converge to zero roughly like n^(-1/2). Under these conditions the Bonferroni correction is ABOS in case of extreme sparsity, while BH adapts well to the unknown level of sparsity. In the second part of this article these optimality results are carried over to model selection in the context of multiple regression with orthogonal regressors. Several modifications of Bayesian Information Criterion are considered, controlling either FWER or FDR, and conditions are provided under which these selection criteria are ABOS. Finally the performance of these criteria is examined in a brief simulation study.